Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "96" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 18 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 18 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | not_connected | 100.00% | 99.14% | 99.08% | 0.00% | - | - | 251.955874 | 251.627298 | inf | inf | 4186.439901 | 4185.959227 | 7066.612692 | 7101.893707 | 0.5205 | 0.5209 | 0.4488 | nan | nan |
| 2459994 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.619897 | 20.118194 | 3.535531 | 1.483888 | 3.510631 | 3.158496 | -1.963203 | -1.309265 | 0.5950 | 0.5015 | 0.3515 | nan | nan |
| 2459991 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.444201 | 24.461386 | 3.776443 | 1.698372 | 3.895162 | 2.943339 | -2.459306 | -1.433049 | 0.5966 | 0.4938 | 0.3694 | nan | nan |
| 2459990 | not_connected | 100.00% | 97.62% | 97.68% | 0.00% | - | - | 173.131044 | 172.444452 | inf | inf | 3299.726089 | 3276.069790 | 6240.600777 | 6119.960644 | 0.3854 | 0.4351 | 0.2799 | nan | nan |
| 2459989 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.638033 | 21.733882 | 3.590674 | 1.522982 | 3.215096 | 1.973578 | -2.386404 | -1.762020 | 0.5925 | 0.4932 | 0.3646 | nan | nan |
| 2459988 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.382239 | 24.747399 | 3.888099 | 1.870198 | 4.420959 | 3.923584 | -2.388506 | -1.377784 | 0.5933 | 0.4989 | 0.3581 | nan | nan |
| 2459987 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.401063 | 19.522256 | 3.549614 | 1.509646 | 2.627410 | 2.050454 | -3.349587 | -1.881287 | 0.6014 | 0.5083 | 0.3545 | nan | nan |
| 2459986 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.555384 | 23.521306 | 3.897869 | 1.831522 | 4.122794 | 3.644248 | 1.129553 | 2.056870 | 0.6177 | 0.5398 | 0.3159 | nan | nan |
| 2459985 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.003667 | 21.339326 | 3.462352 | 1.545643 | 3.173141 | 2.284380 | -3.860962 | -2.606521 | 0.6013 | 0.5083 | 0.3574 | nan | nan |
| 2459984 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.713998 | 18.014385 | 3.547655 | 1.694252 | 4.928443 | 4.766650 | -1.012766 | -0.578888 | 0.6121 | 0.5356 | 0.3394 | nan | nan |
| 2459983 | not_connected | 100.00% | 0.00% | 0.00% | 93.19% | - | - | 24.634930 | 2.738422 | 0.811018 | 2.854558 | 2.564138 | 5.035964 | 3.381826 | 1.822341 | 0.3442 | 0.3291 | -0.2282 | nan | nan |
| 2459982 | not_connected | 100.00% | 0.00% | 0.00% | 92.06% | - | - | 19.362156 | 0.471413 | 0.088278 | 1.856787 | 0.832692 | 1.083527 | 0.341042 | 1.022942 | 0.3861 | 0.3637 | -0.2148 | nan | nan |
| 2459981 | not_connected | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 24.275696 | 2.610225 | 0.968993 | 3.372681 | 10.647848 | 10.472751 | 1.145069 | -1.335205 | 0.2491 | 0.2212 | -0.2621 | nan | nan |
| 2459980 | not_connected | 100.00% | 0.00% | 0.00% | 97.73% | - | - | 10.418021 | 5.594051 | 3.083562 | 4.238692 | 4.263200 | 8.653397 | 2.283369 | 3.990349 | 0.3381 | 0.2950 | -0.2212 | nan | nan |
| 2459979 | not_connected | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 11.826610 | 6.286600 | 3.090504 | 4.239420 | 3.827698 | 7.941063 | -1.688729 | -3.426442 | 0.2494 | 0.2109 | -0.2539 | nan | nan |
| 2459978 | not_connected | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 12.268894 | 6.516440 | 3.421427 | 4.750043 | 3.885474 | 8.777623 | -2.368223 | -4.044642 | 0.2423 | 0.1993 | -0.2563 | nan | nan |
| 2459977 | not_connected | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 10.810812 | 6.405774 | 2.989011 | 4.058133 | 4.577110 | 9.378942 | -2.713903 | -3.875131 | 0.2498 | 0.2083 | -0.2349 | nan | nan |
| 2459976 | not_connected | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 11.802273 | 6.396478 | 3.326196 | 4.670810 | 3.584395 | 8.475789 | -1.221134 | -2.395465 | 0.2599 | 0.2129 | -0.2532 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Power | inf | 251.955874 | 251.627298 | inf | inf | 4186.439901 | 4185.959227 | 7066.612692 | 7101.893707 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 20.118194 | 4.619897 | 20.118194 | 3.535531 | 1.483888 | 3.510631 | 3.158496 | -1.963203 | -1.309265 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 24.461386 | 5.444201 | 24.461386 | 3.776443 | 1.698372 | 3.895162 | 2.943339 | -2.459306 | -1.433049 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Power | inf | 172.444452 | 173.131044 | inf | inf | 3276.069790 | 3299.726089 | 6119.960644 | 6240.600777 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 21.733882 | 21.733882 | 4.638033 | 1.522982 | 3.590674 | 1.973578 | 3.215096 | -1.762020 | -2.386404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 24.747399 | 24.747399 | 5.382239 | 1.870198 | 3.888099 | 3.923584 | 4.420959 | -1.377784 | -2.388506 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 19.522256 | 4.401063 | 19.522256 | 3.549614 | 1.509646 | 2.627410 | 2.050454 | -3.349587 | -1.881287 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 23.521306 | 23.521306 | 5.555384 | 1.831522 | 3.897869 | 3.644248 | 4.122794 | 2.056870 | 1.129553 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 21.339326 | 21.339326 | 5.003667 | 1.545643 | 3.462352 | 2.284380 | 3.173141 | -2.606521 | -3.860962 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | nn Shape | 18.014385 | 4.713998 | 18.014385 | 3.547655 | 1.694252 | 4.928443 | 4.766650 | -1.012766 | -0.578888 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 24.634930 | 24.634930 | 2.738422 | 0.811018 | 2.854558 | 2.564138 | 5.035964 | 3.381826 | 1.822341 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 19.362156 | 19.362156 | 0.471413 | 0.088278 | 1.856787 | 0.832692 | 1.083527 | 0.341042 | 1.022942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 24.275696 | 2.610225 | 24.275696 | 3.372681 | 0.968993 | 10.472751 | 10.647848 | -1.335205 | 1.145069 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 10.418021 | 5.594051 | 10.418021 | 4.238692 | 3.083562 | 8.653397 | 4.263200 | 3.990349 | 2.283369 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 11.826610 | 11.826610 | 6.286600 | 3.090504 | 4.239420 | 3.827698 | 7.941063 | -1.688729 | -3.426442 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 12.268894 | 6.516440 | 12.268894 | 4.750043 | 3.421427 | 8.777623 | 3.885474 | -4.044642 | -2.368223 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 10.810812 | 10.810812 | 6.405774 | 2.989011 | 4.058133 | 4.577110 | 9.378942 | -2.713903 | -3.875131 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96 | N11 | not_connected | ee Shape | 11.802273 | 6.396478 | 11.802273 | 4.670810 | 3.326196 | 8.475789 | 3.584395 | -2.395465 | -1.221134 |